Programmable self-assembly in a thousandrobot

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1 Programmable self-assembly in a thousandrobot swarm Michael Rubenstein, Alejandro Cornejo, Radhika Nagpal. By- Swapna Joshi 1 st year Ph.D Computing Culture and Society.

2 Authors Michael Rubenstein Assistant Professor Department of Electrical Engineering and Computer Science Department of Mechanical Engineering Northwestern University Alejandro Cornejo Tech Start Up PhD in C.S - MIT Radhika Nagpal Fred Kavli Prof. of Computer Sc. School of Eng and Applied Sc. Wyss Institute for Biologically Inspired Engineering Harvard University

3 COMPUTATIONAL BEAUTY- In Nature Some social systems in Nature can present an intelligent collective behavior although they are composed by simple individuals. The intelligent solutions to problems naturally emerge from the self-organization and communication of these individuals. These systems provide important techniques that can be used in the development of artificial intelligent systems.

4 COLLECTIVE INTELLIGENCE- learning from nature Rules of Engagement: One vs Collective Any one individual could have a really limited view of what is going on Intelligence is not limited to individual, but property of the group No leadership Sheer oneness, emerging from interactions or local rules of engagement Single entity, single mind making collective decisions Local and simple interactions New properties emerge, such as phase transition, pattern formation, group movement

5 COLLECTIVE INTELLIGENCE Inspiration A lot of my research is built around this idea that if you have a collective of individuals and they all have simple local rules, what you cant do is design the rules bottom up. Because you are just going to be stuck trying to see every variation of what goes on, and if you look what an individual is doing, it is not well connected with the global one. So is there a way to go inverse? Is it possible to write, like, a compiler where I say, Well, what I want the group to achieve is this. And then, the computer sort of figures out what it is that all of the individuals should do. Nagpal IEEE History of Robotics Interviews Conducted by Prof.Selma Sabanovic

6 COLLECTIVE INTELLIGENCE Intelligent Machines A key theme in AI is to create computational intelligence the way we see it in nature. Creating these rules of engagement and local interactions for computing collective intelligence. Two main questions: How do we take a global goal and translate it into local interactions between identical agents? How do we engineer robust, predictable behavior from large number of unreliable agents?

7 Thousand robot swarm Created a colony of thousand simple robots to exhibit collective intelligence. Programmable. Wirelessly communicates with other robots and measures distances from them. Programming different (nature like) rules of engagement such as Synchrony Pattern determination Migration.

8 Algorithms Design Creation of complicated self assemblies by combining different rules of engagement. 1. Robots have the ability to approximate holonomic motion (move straight, turn in place). 2. Robots can communicate with neighboring robots within a fixed radius. 3. Robots can measure distance to communicating neighbors within that radius. 4. Robots have basic computation capabilities and internal memory. All robots, except the four seed robots, are given an identical program, which includes the selfassembly algorithm and a description of the desired shape.

9 Algorithms Edge-following In edge-following, a moving robot attempts to follow the boundary of a set of stationary robots in a clockwise direction.

10 Algorithms Gradient Formation Individual robots can measure distances between each other; the purpose of gradient formation is to create a long-range sense of distance across a swarm.

11 Algorithms Localization The self-assembly algorithm relies on robots ability to localize in a coordinate system that is generated and shared by robots inside the desired shape.

12 Algorithm State Diagram

13 Algorithms Self Assembly Algorithm Any single robot is talking to a small number of robots nearby it, using its motion rule to move around the half built structure to decide a place to fit in based on its pattern rules.

14 Algorithms Shape self Assembly Creation of complicated self assemblies by combining different rules of engagement. Any single robot is talking to a small number of robots nearby it, using its motion rule to move around the half built structure to decide a place to fit in based on its pattern rules. Thousand Robots Swarm

15 Algorithms Accuracy Even though no robot is doing anything accurately, the rules are such that the collective achieves its goal, working like a single entity rather than individuals.

16 Collective intelligence Other projects 3Dimensions Inspirations from Social insects that use pattern rules that help them determine what to build.

17 Collective Intelligence Application conceptualizations Many different applications are possible by creating such artificial collective intelligence or computing them. They are mathematical and conceptual tools to create our own versions of collective power.

18 COLLECTIVE INTELLIGENCE Questions What are some of the ethical considerations to computing collective intelligence or articficially creating it? link In what ways is human collective behavior different from that of other organisms? What about rules that apply to our own human collective? Should we ever engineer the human collective? What could errors mean in the context of human collective?

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